A Novel Method for Segmentation of QRS Complex on ECG Signals and Classify Cardiovascular Diseases via a Hybrid Model Based on Machine Learning

Sehirli, Eftal; Turan, Muhammed Kamil

doi:10.18201/ijisae.2021167932

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…Yaman O et al [48] attained 91.25% precision utilizing SVM and KNN approach. Simjanoska et al [49] attained 93.5% accuracy using CNN. Yaman O et al [48] attained 91.25% precision utilizing SVM and KNN approach.…”

Section: Simulation Resultsmentioning

confidence: 99%

Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning

Asif,

Ditta,

Alquhayz

et al. 2024

IEEE Access

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In this study, a weighted federated learning approach is proposed for electrocardiogram (ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data distribution among multiple clients in federated learning settings. The weight of each client is dynamically adjusted according to its contribution to the global model improvement. Experiments on public ECG datasets show that the proposed approach outperforms traditional federated learning and centralized learning methods in terms of accuracy and robustness. On the client side, the suggested federated learning (FL) approach had an accuracy of 0.93, sensitivity of 0.98, specificity of 0.82, miss classification rate of 0.07, precision of 0.06, FPR of 0.01, and FNR of 0.01. FL has 0.98 accuracy, 0.99 sensitivity, 0.91 specificity, 0.02 miss classification rate, 0.10 precision, 0.01, FPR, and 0.01 FNR on the server. The server-side federated learning approach outperforms the client-side in accuracy, sensitivity, specificity, miss classification rates, and precision. The results indicate that the proposed weighted federated learning approach is a promising solution for ECG arrhythmia classification in a distributed environment. In short, the proposed federated learning approach applied to ECG arrhythmia detection aims to address privacy concerns and improve accuracy, while still maintaining the centralized framework and advanced algorithmic approach.

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Section: Simulation Resultsmentioning

confidence: 99%

Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning

Asif,

Ditta,

Alquhayz

et al. 2024

IEEE Access

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“…e assessment and development of the entire program are to be carried out in the MATLAB 2021a with 11th Gen Intel(R) Core (TM) i5-1135G7 @ 2.40 GHz computer processor, 8.00 GB RAM, and 1 TB hard disk along with also tested on Raspberry Pi 3.0+, Arduino, and heart monitor on run time. Performance evaluation of algorithms is evaluated with different statistical parameters as shown in the following equations adapted from [27][28][29][30][31][32][33]45]:…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…Gelan Ayana et al proposed the ImageNet transfer learning method to detect breast cancer for detection and diagnosis and achieved accuracy better as compared to the previous research [ 24 – 27 ]. The deep learning approach with the recurrent neural network (RNN) is useful in the ECG rhythm classifier for the sequence modeling of imbalanced data and further compared the performance of the RNN with the long short-term memory (LSTM) and gated recurrent unit (GRU) and observed that the LSTM technique is the latent method for the sequential data with an accuracy of 97.7% [ 28 – 31 ]. In addition, researchers proposed the validation of ECG-derived sleep architecture and ventilation in sleep apnea and chronic fatigue syndrome and analyzed the result by using the kappa score, which is 0.68, 0.85, and 0.69 for different classes [ 30 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

Asif

Abbas

Khan

et al. 2022

Computational Intelligence and Neuroscience

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With the emergence of the Internet of Things (IoT), investigation of different diseases in healthcare improved, and cloud computing helped to centralize the data and to access patient records throughout the world. In this way, the electrocardiogram (ECG) is used to diagnose heart diseases or abnormalities. The machine learning techniques have been used previously but are feature-based and not as accurate as transfer learning; the proposed development and validation of embedded device prove ECG arrhythmia by using the transfer learning (DVEEA-TL) model. This model is the combination of hardware, software, and two datasets that are augmented and fused and further finds the accuracy results in high proportion as compared to the previous work and research. In the proposed model, a new dataset is made by the combination of the Kaggle dataset and the other, which is made by taking the real-time healthy and unhealthy datasets, and later, the AlexNet transfer learning approach is applied to get a more accurate reading in terms of ECG signals. In this proposed research, the DVEEA-TL model diagnoses the heart abnormality in respect of accuracy during the training and validation stages as 99.9% and 99.8%, respectively, which is the best and more reliable approach as compared to the previous research in this field.

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A new approach based on a 1D + 2D convolutional neural network and evolving fuzzy system for the diagnosis of cardiovascular disease from heart sound signals

Xiao,

Liu,

2024

Applied Acoustics

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A Novel Method for Segmentation of QRS Complex on ECG Signals and Classify Cardiovascular Diseases via a Hybrid Model Based on Machine Learning

Cited by 10 publications

References 28 publications

Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning

Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning

Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning

A new approach based on a 1D + 2D convolutional neural network and evolving fuzzy system for the diagnosis of cardiovascular disease from heart sound signals

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